Artificial Intelligence and Decision Making
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Artificial Intelligence and Decision Making, 2025, Issue 1, Pages 103–114
DOI: https://doi.org/10.14357/20718594250109 (Mi iipr621) 		 
This article is cited in 2 scientific papers (total in 2 papers)

Analysis of signals, audio and video information

Application of machine learning methods to image analysis of chronic wounds

A. G. Nazarenkoa, E. B. Kleymenovaa, A. I. Molodchenkovbc, A. S. Ponomarchukd, N. P. Gerasimovaa, E. S. Yurchenkovaa, L. P. Yashinaa

a National Medical Research Center of Traumatology and Orthopedics named after N.N. Priorova, Moscow, Russia
b Peoples' Friendship University of Russia named after Patrice Lumumba, Moscow, Russia
c Federal Research Center "Computer Science and Control" of Russian Academy of Sciences, Moscow, Russia
d National Research University Higher School of Economics, Moscow
Full-text PDF (649 kB) Citations (2)
DOI: https://doi.org/10.14357/20718594250109
Abstract: Neural networks and deep learning algorithms are increasingly used in medicine, including image analysis. In surgery, soft tissue wounds assessment remains challenging but necessary issue to assess the course of healing process and treatment effectiveness. Digital wound images are used for noncontact wound analysis. The paper presents the results of pre-trained network models (AlexNet, ResNet50, ResNet152, VGG16) used to classify pressure ulcer images as examples of chronic wounds. The Segment Anything Model (SAM) demonstrated an accuracy of 86.46% in solving the problem of segmenting the edges of a wound defect and tissue types within it. The results can be used to create an expert system for analyzing soft tissue wound images.
Keywords: artificial intelligence, machine learning, image recognition, pressure ulcers, wounds.
Funding agency Grant number
Russian Science Foundation 24-14-00310
Bibliographic databases:
Document Type: Article
Language: Russian
Citation: A. G. Nazarenko, E. B. Kleymenova, A. I. Molodchenkov, A. S. Ponomarchuk, N. P. Gerasimova, E. S. Yurchenkova, L. P. Yashina, “Application of machine learning methods to image analysis of chronic wounds”, Artificial Intelligence and Decision Making, 2025, no. 1, 103–114
Citation in format AMSBIB
\Bibitem{NazKleMol25}
\by A.~G.~Nazarenko, E.~B.~Kleymenova, A.~I.~Molodchenkov, A.~S.~Ponomarchuk, N.~P.~Gerasimova, E.~S.~Yurchenkova, L.~P.~Yashina
\paper Application of machine learning methods to image analysis of chronic wounds
\jour Artificial Intelligence and Decision Making
\yr 2025
\issue 1
\pages 103--114
\mathnet{http://mi.mathnet.ru/iipr621}
\crossref{https://doi.org/10.14357/20718594250109}
\elib{https://elibrary.ru/item.asp?id=80493705}
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  • https://www.mathnet.ru/eng/iipr/y2025/i1/p103
    • This publication is cited in the following 2 articles:
    Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
    		Artificial Intelligence and Decision Making
    Statistics & downloads:
    Abstract page:303
    Full-text PDF :114
    First page:26
     
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